A hybrid approach with MPPT controller for weed cutting based on solar powered lawnmower with minimal intervention of human involvement adopting IoT technology

Abstract

A novel hybrid method is proposed for designing a highly autonomous solar-powered lawnmower. The proposed hybrid method is a combination of the pelican optimization algorithm (POA) and the random forest algorithm (RFA); commonly, it is named the POARFA technique. The key objective of the proposed technique is to minimize errors while ensuring smooth and reliable operation. The solar lawnmower includes a rechargeable battery, Internet of Things (IoT), solar panel, and DC motor for control, monitoring, and user information. The IoT is utilized to control, monitor, and provide information to the user. The key components of the proposed lawnmower include a rechargeable battery, solar panel, IoT, and DC motor. This electrical energy is fed into the charging circuit. The controller of fractional order proportional integral derivative (FOPID) is used to regulate the motor that is utilized to track the path and improve the response of the system. The RFA approach is used to tune the parameters of the FOPID controller. The proposed solar lawnmower is extremely versatile, very durable, comfortable, and powerful, evading obstacles on the path. The proposed technique is executed in the MATLAB software and is compared with existing techniques. The peak overshoot of the POARFA approach is 0.712%, significantly lower than other approaches. In conclusion, the proposed POARFA approach showcases promising results for solar-powered lawnmowers, offering a more efficient, reliable, and sustainable solution compared to existing methods.